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Baca Mariacutea Laumlderach Peter Haggar Jeremy Schroth Goumltz and Ovalle Oreana (2014) An integrated framework for assessing vulnerability to climate change and developing adaptation strategies for coffee growing families in Mesoamerica PLOS ONE 9 (2) e88463 ISSN 1932-6203 (doi101371journalpone0088463)

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Baca Mariacutea Laumlderach Peter Haggar Jeremy Schroth Goumltz and Ovalle Oreana (2014) An integrated framework for assessing vulnerability to climate change and developing adaptation strategies for coffee growing families in Mesoamerica London Greenwich Academic Literature ArchiveAvailable at httpgalagreacuk11339

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An Integrated Framework for Assessing Vulnerability toClimate Change and Developing Adaptation Strategiesfor Coffee Growing Families in Mesoamerica

Marıa Baca1 Peter Laderach1 Jeremy Haggar2 Gotz Schroth3 Oriana Ovalle4

1 International Center for Tropical Agriculture (CIAT) Managua Nicaragua 2Natural Resources Institute (NRI) University of Greenwich Chatham Maritime Kent United

Kingdom 3 Rainforest Alliance Wageningen The Netherlands 4 International Center for Tropical Agriculture (CIAT) Cali Colombia

Abstract

The Mesoamerican region is considered to be one of the areas in the world most vulnerable to climate change Wedeveloped a framework for quantifying the vulnerability of the livelihoods of coffee growers in Mesoamerica at regional andlocal levels and identify adaptation strategies Following the Intergovernmental Panel on Climate Change (IPCC) conceptsvulnerability was defined as the combination of exposure sensitivity and adaptive capacity To quantify exposure changesin the climatic suitability for coffee and other crops were predicted through niche modelling based on historical climatedata and locations of coffee growing areas from Mexico Guatemala El Salvador and Nicaragua Future climate projectionswere generated from 19 Global Circulation Models Focus groups were used to identify nine indicators of sensitivity andeleven indicators of adaptive capacity which were evaluated through semi-structured interviews with 558 coffee producersExposure sensitivity and adaptive capacity were then condensed into an index of vulnerability and adaptation strategieswere identified in participatory workshops Models predict that all target countries will experience a decrease in climaticsuitability for growing Arabica coffee with highest suitability loss for El Salvador and lowest loss for Mexico Highvulnerability resulted from loss in climatic suitability for coffee production and high sensitivity through variability of yieldsand out-migration of the work force This was combined with low adaptation capacity as evidenced by poor post harvestinfrastructure and in some cases poor access to credit and low levels of social organization Nevertheless the specificcontributors to vulnerability varied strongly among countries municipalities and families making general trends difficult toidentify Flexible strategies for adaption are therefore needed Families need the support of government and institutionsspecialized in impacts of climate change and strengthening of farmer organizations to enable the adjustment of adaptationstrategies to local needs and conditions

Citation Baca M Laderach P Haggar J Schroth G Ovalle O (2014) An Integrated Framework for Assessing Vulnerability to Climate Change and DevelopingAdaptation Strategies for Coffee Growing Families in Mesoamerica PLoS ONE 9(2) e88463 doi101371journalpone0088463

Editor Ben Bond-Lamberty DOE Pacific Northwest National Laboratory United States of America

Received July 11 2013 Accepted January 7 2014 Published February 26 2014

Copyright 2014 Baca et al This is an open-access article distributed under the terms of the Creative Commons Attribution License which permits unrestricteduse distribution and reproduction in any medium provided the original author and source are credited

Funding This research was conducted under the CGIAR Research Program on Climate Change Agriculture and Food Security (CCAFS) in collaboration withCatholic Relief Services (CRS) and additional funding from Green Mountain Coffee Roasters (GMCR) Funds were used for employment and development ofresearch The funders had no role in study design data collection and analysis decision to publish or preparation of the manuscript There are no further patentsproducts in development or marketed products to declare

Competing Interests Funding from Green Mountain Coffee Roasters (GMCR) Funds were used for employment and development of research There are nofurther patents products in development or marketed products to declare This does not alter the authors9 adherence to all the PLOS ONE policies on sharingdata and materials as detailed online in the guide for authors

E-mail magubagogmailcom

Introduction

Climate change represents a serious threat for Mesoamerican

countries due to the multiple impacts predicted to directly affect

the population as well as various sectors of the economy [1] [2]

The Vulnerability-Resilience Indicators developed by Yohe

suggested high exposure to climate change for the Mesoamerican

and Caribbean Region [14] Climate projections indicate that

increases in temperature will reduce crop yields in general [3] and

particularly those of Arabica coffee one of the regionrsquos major

exports Arabica coffee responds strongly to seasonal temperature

patterns and coffee of high quality requires relatively stable

temperatures within a fairly narrow range [4] [5] Laderach and

others [6] predicted that optimal conditions for growing Arabica

coffee in Mesoamerica will move from currently 800 to 1400

masl upwards to 1200 to 1600 masl by 2050 Studies in

Ethiopia and Kenya have similarly foreseen significant impacts of

climate change on the distribution of wild coffee and coffee pests

[7] [8] in the later case extending the areas affected by coffee

berry-borer

During the last 40 years agriculture has contributed 10 of the

GDP in Latin American countries and is a major export earner It

is an important sector in the regional economy since it employs

30 to 40 of the economically active population and is essential

for the food security of the poorest segment of society [3] [9]

Across Mexico and Central America over 4 million people

depend directly on coffee production for their livelihoods [10]

According to CEPAL [9] coffee production purchasing and

processing employ an estimated 85 million people in the region

Employment and income generation from coffee are particularly

significant for many indigenous peoples in Mexico and Guate-

mala The environmental services generated by shade coffee

farms including carbon sequestration watershed services and the

conservation of biodiversity have also been highlighted by many

PLOS ONE | wwwplosoneorg 1 February 2014 | Volume 9 | Issue 2 | e88463

authors [11] [12] while Haggar and others [13] have shown how

land-use change can affect the provision of environmental services

when shade coffee is replaced by other land-uses [13]

The Intergovernmental Panel on Climate Change (IPCC)

presents an integrated concept where lsquolsquovulnerability to climate

change is the degree by which a system is susceptible or unable to

face the adverse effects of climate change including climate

extremes and variability Moreover vulnerability depends on the

nature magnitude and rate of climate change as well as the

variation to which a system is exposed its sensitivity and its

capacity for adaptationrsquorsquo Exposure is the nature and extent of

changes that a placersquos climate is subjected to with regard to

variables such as temperature precipitation and extreme weather

events Sensitivity is a measure of how systems could be affected by

the change in climate (eg how much crop yields change or how

much human health might be affected) In contrast adaptive

capacity is defined as a systemrsquos ability to adjust to climate change

in order to reduce or mitigate possible damage [3] Adaptive

capacity is dynamic and depends partly on the society productive

base such as natural and artificial assets social benefits and

networks human capital and institutions governance national

income health and technology [2] and how much capability a

society has to adapt to the changes so as to maintain minimize loss

of or maximize gain in welfare

The current study was conducted to evaluate the vulnerability of

coffee farming communities in El Salvador Guatemala Mexico

and Nicaragua and to identify adaptation strategies to climate

change [1] [3]

Materials and Methods

In order to assess the vulnerability to climate change and define

appropriate adaptation strategies we adapted the IPCCrsquos defini-

tion of vulnerability and applied it to small coffee producers [3]

For our methodology vulnerability is defined as changes in

climate variables that affect agricultural and natural systems over a

Figure 1 Framework to assess the vulnerability of coffee communities and to identify strategies for adaptation to climate changedoi101371journalpone0088463g001

Vulnerability to Climate Change in Coffee Growing


timeframe The vulnerability in the livelihoods of small coffee

farmers is a function of three factors exposure sensitivity and

adaptive capacity

These factors are related to the interaction between climate

change and access and availability of resources to farming families

Exposure is quantified by modelled coffee crop suitability change

comparing current and future climates representing how families

livelihoods will be impacted by climate change Sensitivity and

adaptive capacity are measured by indicators based on family

resources-such as-natural human social physical and financial

capital [1] We quantified vulnerability levels by combining

exposure sensitivity and adaptive capacity Then we identified

adaptation strategies based on vulnerability levels applying

participatory methods with coffee producing communities and

organizations (Figure 1) The communities and organizations

included were from four countries The study was part of a

development project seeking to facilitate adaptation to climate

change among coffee producers that was implemented in Mexico

Nicaragua Guatemala and El Salvador These countries represent

a range of economic and social development in the region from

Nicaragua the poorest to Mexico the richest country

ExposureTo quantify exposure to climate change crop suitability models

predicting future changes of climatic suitability of coffee were used

for the four countries The methodology combined current climate

data with future climate change predictions To map current

climatic suitability the historical climate database WorldClim

(wwwworldclimorg) was used The variables included a total of

19 bioclimatic variables derived from monthly precipitation

monthly median temperature minimum and maximum temper-

ature [15] Bioclimatic variables represent annual trends season-

ality and extreme conditions

To predict future climate the SRES-A2a scenario 19 IPCC

Global Circulation Models were used The Delta method was used

to down-scale the climate change data based on the sum of the

anomalies interpolated with the WorldClim monthly high-

resolution surfaces [15] The method produces a softened surface

(interpolation) of climate change (deltas or anomalies) It implies

that changes in climate are only relevant at coarse-scale and that

the interactions between variables are maintained in the future

[16]

The Maximum entropy (MAXENT) method a general-purpose

method for making predictions or inferences based on incomplete

information [17] was used to predict the future climatic suitability

for coffee The model requires calibration with climate data for

current coffee production areas which is provided by GPS

coordinates The model assumes that a certain future climate at a

given site is as suitable or unsuitable for the crop as is the same

climate at another site in the present This assumption is

reasonable as long as crop genetics and cropping systems do not

significantly change It thus predicts what will happen in terms of

relative climatic suitability for a crop if these factors do not change

and helps identify those sites where adaptations in crops and

cropping systems are necessary in order to avoid the consequences

of a predicted decline in climatic suitability This approach has

previously been used for coffee [6] [18]

Two measures of uncertainty were calculated (1) the agreement

of calculated models as a percentage of models that predict

changes in the same direction and (2) the coefficient of variation

(CV) among models

Sensitivity and adaptive capacityIndicators of the sensitivity to climate change and adaptive

capacity were devised in collaboration with organizations and

experts from the region using an expert panel focus groups and

semi-structured interviews For the expert panel semi-structured

individual interviews were conducted with 17 key informants of

the coffee sector in Nicaragua including technicians farmers and

researchers It included questions about the most important factors

affecting coffee production Four focus groups were carried out in

Nicaragua and three groups in each of the remaining countries (El

Salvador Guatemala and Mexico) Participants discussed and

assessed the significance of climate change over time and identified

key indicators for coffee livelihoods The list of key indicators was

structured according to the five community capitals (natural

human social physical and financial) of the Livelihoods Approach

[1]

Parameters were then constructed to evaluate each indicator as

shown in Table S1 in File S1 To quantify the parameters scales

from 1 to 5 were applied or a binary scale of 0 and 1 depending

on the nature of the parameter The final values for each indicator

were calculated by averaging all the parameters and then

transformed to a 0-1 continuous variable scale with 0 being low

and 1 being high sensitivity and adaptive capacity For example

access to and availability of water is an important natural resource

for families livelihoods and coffee production To measure the

water access and availability indicator we considered the

parameters source distance quality and quantity of water Water

availability for example is measured on a scale of 1 to 5 1 being

least sensitive and 5 being most sensitive A value of 1 means there

is never sufficient water and a value of 5 means there is an

abundance of water all year Then we developed a semi-structured

Table 1 Number of interviewed families by country andexposure levela

Country

Department or

State

Exposure

level Total

High Medium Low

Nicaragua Jinotega 12 14 15 41

Matagalpa 36 20 5 61

Madriz 4 14 14 32

Nueva Segovia 0 0 16 16

Total 50 50 50 150

El Salvador Usulutan 11 0 9 20

Santa Ana 8 0 0 8

La Libertad 4 11 14 29

Ahuachapan 20 32 20 72

Total 43 43 43 129

Guatemala Chiquimula 14 23 10 47

Solola 17 2 4 23

Chimaltenango 4 5 1 10

San Marcos 8 13 28 49

Total 43 43 43 129

Mexico Chiapas 4 0 32 36

Oaxaca 45 30 11 86

San Luis Potosı 1 20 7 28

Total 50 50 50 150

aFor the definition of exposure levels see section 23doi101371journalpone0088463t001



interview by adapting qualitative tools [19] and validated the tool

with six coffee families

The indicators were used to assess the vulnerability of coffee

farms in each country From a population of 7000 farmer

members from 15 organizations across the four countries 558

farmers were interviewed The farmers may be considered

representative of small-scale organized farmers but should not

be considered representative of the coffee farmers as a whole in

each country The sample size was defined using the formula for

finite populations [20] and then individual farmers were selected

randomly stratified according to exposure level and country by

2050 (Table 1)

VulnerabilityFor exposure the relative decreases in climatic suitability

according to the MAXENT model were divided into three classes

of suitability loss (low medium high) For sensitivity and adaptive

capacity indicators were identified and quantified through

interviews with the farming families

A cluster analysis was carried out for each indicator of sensitivity

and adaptive capacity based on the score of each family using the

Ward method with Euclidean distance Then an Analysis of

Variance (ANOVA) was applied using the LSD-Fisher test to

compare the averages for each indicator by cluster The indicators

in each cluster that obtained significantly different sample averages

were classified in three levels on a scale of 0 to 1 (0ndash033= low

Table 2 Projected changes in overall suitability for coffee production and altitudinal range suitable for production inMesoamerica by 2050

Country

Changes in overall

suitability for coffee

production

Altitude suitable for

production in meters

above sea level

ndash40 or more ndash40 to ndash20 ndash20 to 0 0 Current model Future model

(1950ndash2005) (2050)

El Salvador 455 437 109 0 700 to 1700 1000 to 1700

Guatemala 129 255 542 74 600 to 1800 1200 to 2200

Mexico 182 346 469 03 500 to 2000 1200 to 2300

Nicaragua 353 321 325 01 700 to 1500 1000 to 1600

Adapted from Laderach et al (2010b)doi101371journalpone0088463t002

Figure 2 Prediction of the relative climatic suitability for Arabica coffee production in Mexico Guatemala El Salvador andNicaragua in 2010 and 2050 (large maps) coefficient of variation (CV small map to the left) and consistency between models(small map to the mid-right)doi101371journalpone0088463g002



034ndash066=medium 067ndash1= high) Clusters with the greatest

number of indicators with high medium or low averages were

classified as having high medium or low sensitivity and adaptive

capacity [21]

Each factor (exposure sensitivity and adaptive capacity) as

previously explained and was classified into three levels (high

medium low) To calculate the vulnerability equation we assigned

each level a quantitative value low= 1 medium=2 high= 3

With three factors and three levels per factor we obtained 27

possible combinations After applying the equation we obtained 7

values (ndash1012345) which we used to define low (ndash10) medium

(123) and high (45) levels of vulnerability (Figure 1) A Principal

Components Analysis (PCA) was carried out to identify the

indicators that most contribute to the sensitivity or adaptive

capacity of families in different municipalities

Identifying adaptation strategiesWorkshops were carried out to identify possible adaptation

strategies Participants were families of farmers technicians and

presidents of different cooperatives Workshops began by present-

ing the results of the vulnerability analysis by state department or

municipality After a general discussion the following question was

asked to groups Given that the conditions for coffee production

will change by 2050 what can be done to maintain the level of

production This was first discussed in the plenary and then in

subgroups of 3 to 5 people followed by the presentation of results

Each participant then noted the three most important ideas from

the discussion on separate cards and assigned them to one of the

five types of resources (natural human social physical and

financial) where he felt they most closely linked Then subgroups

were formed for each resource type to organize the ideas and build

an outline of a climate change adaptation strategy In this strategy

key actors roles resource availability and time needed to

implement the strategy were identified The subgroups presented

their results to the entire group and received feedback until a

general consensus was reached for each adaptation strategy

Results

ExposureAccording to the climate change models total annual precip-

itation in all countries is predicted to decrease by 2050 Nicaragua

already the driest of the four countries is predicted to have a 5

precipitation decrease by 2050 The mean annual temperature will

increase by 23uC while the maximum temperature will increase

in all countries by between 22uC and 24uC (Table S2 in File S1)

The most decisive climatic variables for the predicted decrease in

climatic suitability for coffee were the increase in maximum mean

temperature of the hottest month Mexico is predicted to reach a

mean maximum temperature of 36uC by 2050

In interviews the producers confirmed the trend of the climate

models (Table S2 in File S1) They mentioned changes in climate

seasonality and predictability including hotter and longer dry

seasons (from three-four months to four-six months) and shorter

and drier wet seasons Also they perceived an increase in extreme

temperatures drought and wind as well as changes in the intensity

and distribution of precipitation They also highlighted an increase

in extreme weather events such as cold periods hail drought and

hurricanes

The climatic suitability for Arabica coffee has been predicted to

decrease in the lowest altitude areas as a result of the increase in

temperature to which coffee quality is sensitive [22] Changes in

temperature and rainfall will decrease the area suitable for coffee

and effectively displace coffee up the altitudinal gradient to cooler

climates On a national level El Salvador and Nicaragua have the

highest percentage of land affected by decreases in suitability of

40 or greater (Table 2) Models predicted that in Central

America as a whole the optimal coffee-growing elevation will shift

from 1200 masl currently to 1600 masl by 2050 [6] [23]

In general the climatic suitability for Arabica coffee will be

maintained at the highest altitudes In Nicaragua the area with

the greatest loss in suitability is located in the Pacific zone in the

departments of Carazo and Managua while the highlands around

Apanas Lake in Jinotega will maintain a high suitability for coffee

quality In El Salvador the area with loss of suitability will be the

departments of San Miguel and Usulutan while the department of

Ahuachapan will maintain high suitability In Guatemala greatest

loss of suitability will be the southern slope of the Pacific volcanic

chain northern Zacapa and eastern Chiquimula while Chimalte-

nango will maintain high suitability In Mexico areas with greatest

loss of suitability will be northern Chiapas Veracruz and Tabasco

whereas high suitability will be maintained in the Chiapas

highlands (Figure 2)

Table 3 Distribution of families per level sensitivity between countries

Country High sensitivity () Medium sensitivity () Low sensitivity () Total ()

Nicaragua 22 61 17 100

El Salvador 40 26 34 100

Guatemala 49 37 14 100

Mexico 23 46 31 100

doi101371journalpone0088463t003

Figure 3 Sensitivity indicators in the livelihoods of smallcoffee producers to climate change in four countries ofMesoamerica (a high value equals high sensitivity)doi101371journalpone0088463g003



Sensitivity and adaptive capacitySensitivity The sampled families from each country were

divided into three sensitivity levels using cluster analysis on

average 33 of the families fell into the highly sensitive level The

families of El Salvador and Guatemala were more likely to cluster

in the high sensitivity level while those of Nicaragua and Mexico

were more likely to cluster in the medium sensitivity class

Guatemala had the highest percentage of families with high

sensitivity (49) and El Salvador the highest percentage with low

sensitivity (34) (Table 3)

An ANOVA test indicated that there are significant differences

(p0001) between high medium and low clusters for the

sensitivity indicators for each country Figure 3 shows some

indicators that were significantly different in the high sensitivity

level compared by cluster for each country The families in the

high sensitivity level in each country were characterized by high

yield variability In El Salvador Guatemala and Nicaragua this

indicator was significantly higher for high sensitivity than for

medium or low sensitivity clusters (p0001) In contrast in

Mexico the indicator was not significantly different (p = 0090)

between sensitivity clusters (Figure 3) Yield variability leads to

frequent reductions in income and the ability of the families to

respond to external stresses such as climate change Furthermore

in Nicaragua Mexico and Guatemala the migration indicator was

also significantly greater for the high sensitivity cluster in each

country (p0001) Migration being the temporary or permanent

work-related move of one or more family members to a foreign

Figure 4 Principal components analysis of association of sensitivity indicators with different municipalitiesdoi101371journalpone0088463g004

Table 4 Distribution of families per level adaptive capacity between countries

Country High adaptive capacity () Medium adaptive capacity () Low adaptive capacity () Total ()




Mexico 38 38 24 100




country reduces the availability of family labour and thus the

resilience to respond to climate change impacts The resulting lack

of labour for the coffee harvest was frequently mentioned during

the interviews

In El Salvador a notable contributor to high sensitivity was lack

of conservation practices (p0001) including lack of maintenance

of vegetation protecting water sources and communally or

individually managed forest areas In parts of the country farm

sizes are small (between 1ndash2 hectares) and therefore there is little

space for leaving areas of natural vegetation On the other hand

producers in some communities in the departments of Ahuacha-

pan and La Libertad in El Salvador are organized in groups of 40ndash

50 families that collectively own 600ndash1100 hectares of land on

which they maintain water sources and forest areas In Mexico

high sensitivity was particularly related to difficulties with

transportation of products (p0001) (Figure 3)

The Principal Components Analysis showed that key indicators

of vulnerability differed among municipalities even within the

same country The value of the first axis and second axis per

country is shown in the Figure 4 The variables that were highly

correlated with axis 1 included yield variability (r = 048) in

Nicaragua road type (r = 048) in El Salvador transport of

products (r = 046) in Guatemala and health and nutrition

(r = 046) in Mexico For example poor transport and bus

connections restricting market access was a key factor of

vulnerability in San Lucas Toliman in Solola Guatemala Farmers

grow coffee among the volcanoes surrounding Lake Atitlan and

had to walk between 3ndash4 hours from their farms to the nearest

market town transporting their produce either on mules or on

Figure 5 Adaptive capacity indicators in the livelihoods ofsmall coffee producers to climate change in four countries ofMesoamerica (a low value equals low adaptive capacity)doi101371journalpone0088463g005

Figure 6 Principal components analysis of the association of adaptive capacity indicators to different municipalitiesdoi101371journalpone0088463g006



their backs Similar conditions prevail in many coffee communities

in remote mountain areas across the region

In El Tuma-La Dalia in Matagalpa the coffee yield of producer

farms had high variability between years Ranges in yield were

between +ndash 25 on average for four years with a variability of

+ndash 50 reported in some cases the average farmer yield was

331 Kgha in contrast with the Matagalpan department average

of 538 Kgha High variability of yields and thus income between

years limited the access and availability of food nutrition

education and health of families Sometimes when the yield was

very low in Nicaragua the families migrate to other places in

search of casual labour Depending on the municipality or

community migration was an important factor in each country

and included both seasonal and permanent migrants (eg in

Matagalpa Nicaragua ten out of eleven interviewed families had

some of its members living abroad as migrants) (Figure 4)

Adaptive capacity The sampled families from each of the

four countries were divided into three adaptive capacity levels

using the cluster analysis on average 32 of families fell into the

low adaptive capacity level The families of Nicaragua El Salvador

and Guatemala were more likely to cluster in the low adaptive

capacity level (Table 4)

The ANOVA test indicated significant differences between the

high medium and low adaptive capacity clusters in each country

but the indicators varied across countries Figure 5 shows

indicators that were significantly different in low adaptive capacity

level compared to clusters for each country The families in the

low adaptive capacity cluster in Mexico Guatemala El Salvador

and Nicaragua were characterized by low viability of post harvest

infrastructure for drying coffee Low adaptive capacity families in

Mexico and El Salvador had poorer post harvest infrastructure for

drying coffee (p0001) than higher adaptive capacity families

Between 50 and 80 of families interviewed had only a single

technique for drying coffee irrespective of weather conditions

While in Mexico El Salvador and Guatemala producers had

access to drying patios in Nicaragua producers dry their coffee

using drying tables drying patios or plastic tarps and in Mexico

producer dry coffee using sacks or on the floors or house roofs In

very humid regions these drying techniques were not suitable due

to lack of adequate sun as a result the drying quality is poor

Some cooperatives in Nicaragua and Guatemala with economic

resources transport the coffee to drier areas while other

cooperatives use a mechanical drying process In both cases this

increases the cost of processing

Furthermore low adaptive capacity farmers in Nicaragua

presented lower income diversification (p0005)Families de-

pended on coffee sales for between 50 to 60 of their yearly

income Some families diversify their income with basic grains

bananas oranges and avocados but the intermediaries pay very

little and the markets are distant In all countries farmers received

credit from cooperatives However in Guatemala low adaptive

capacity farmers had more limited access to credit (p0001) than

higher adaptive capacity farmers with 27 of 129 farmers

interviewed having had no access to credits In Mexico low

adaptive capacity farmers had lower knowledge levels regarding

coffee sector policies and environmental and land use laws

(p0001) than higher adaptive capacity famers In general

interviewed families had knowledge of only one to three coffee

sector or environmental policies and they did not have active

participation in the application of these laws Additionally in

Mexico low adaptive capacity families had low access to

alternative technologies but this result was not significantly

different from the other adaptive capacity levels (p = 0079) For

example they did not collect water for their own consumption or

for crops and they did not use drip irrigation (Figure 5)

There was a close association of some municipalities with

certain indicators in the Principal Components Analysis The

value of the first axis and second axis per country is shown in

Figure 6 The variables that were least correlated with axis 1

included post harvest infrastructure viability (r = ndash009) in Nica-

ragua and (r = ndash0159) Mexico organization (r = ndash043) in El

Salvador and education (r = ndash031) in Guatemala For example in

Xilitla in San Luis de Potosı Mexico interviewed families had little

access to post-harvest infrastructure farmers dry their coffee on

the floor in sacks on plastic or other forms and some producers

sell their coffee as unprocessed cherries with corresponding price

reductions (Figure 6)

In San Lucas Toliman in Solola Guatemala the families had

little access to formal and informal education This is because the

heads of household are of Mayan origin and speak their local

language rather than Spanish They had little access to education

and there is no technical assistance in their own language

Currently children have access to primary school and Spanish

language instruction

Vulnerability and adaptation strategiesThe results of the vulnerability equation indicate that in

Nicaragua of the 143 families 18 had a high level of

vulnerability in El Salvador 14 out of 129 interviewed families

showed high vulnerability in Guatemala 22 out of 129 families

showed high vulnerability and in Mexico 9 out of 150 families

showed high vulnerability (Table 5)

High vulnerability was related to high and medium exposure

which was represented by a loss of climatic suitability for coffee

production but in Mexico this tended to be less severe than in the

Table 5 Percentage of families by vulnerability level in eachcountry

Country

Department or

state Vulnerability level Total ()

High Medium Low


Matagalpa 9 22 6 38

Madriz 3 10 9 22


Total () 18 51 31 100


Santa Ana 2 5 0 6



Total () 14 68 18 100


Solola 9 9 0 18



Total () 22 64 15 100


Oaxaca 9 40 9 57


Total () 9 73 18 100




three countries further south The familiesrsquo farms located in the

high exposure level will not have optimal conditions for

production quality coffee by 2050 (Figure 7)

In addition high vulnerability was related to high and medium

sensitivity which was due to the high variability in productivity

levels The variability of production is very important for the

families because it represents their principal income (on average 50

to 65) and they depend on this income for food security health

and education Furthermore Nicaragua Guatemala and Mexico

had high sensitivity caused by migration Additionally high

vulnerability related to low adaptive capacity resulted in all four

countries from poor post-harvest infrastructure and limited access

to credit and alternative technologies Often the individual

producers did not have access to machines for pulping coffee

drying infrastructure solar dryers drip irrigation or water

harvesting The low level of organization was due to the lack of

participation of many families in joint activities projects training

and exchanges The low level of income diversification was

because many families depend mainly on coffee for cash to buy

food healthcare education and transportation or to invest in the

farms (Table 6)

Families identified as general strategies for adaptation to climate

change the need to develop or improve technologies such as drip

irrigation in areas with high risk of drought shade management

soil fertility management pest and disease control conservation of

soil and ground water and adoption of new crops to adapt to

future conditions

Discussion

The strategy used to determine and integrate estimates of

exposure sensitivity and capacity to adapt to climate change

proved effective to differentiate between high medium and low

vulnerability families and to identify the livelihood characteristics

that contribute to those states Nevertheless factors that contribute

to vulnerability are distinct between departments municipalities

and families This is consistent with Cutters analysis that lsquolsquoit is

place that forms the fundamental unit of analysisrsquorsquo for vulnerability

[24] Thus it is not unexpected that the nature of vulnerability is

very site or even family specific [25]

The farms located in areas with high vulnerability level will not

have suitable conditions for quality coffee production by 2050

These conditions include changing climate factors (eg tempera-

ture precipitation) by 2050 high variability of coffee production

and high levels of migration in some communities low adaptive

capacity in post harvest infrastructure and in Guatemala and

Mexico low access to credit Some of these are similar to those

identified by Eakin [26] as the important drivers of change which

include torrential rainfall credit declining soil fertility new

market opportunities and declining international coffee price

Tucker found that coffee farmers in Central America primarily

adapted to global change through changes in crops and crop

Figure 7 Small-scale variability of vulnerability to climate change among coffee producing communities in four countries ofMesoamericadoi101371journalpone0088463g007



practices supporting the importance of diversification options

Nevertheless these changes were not associated with perceptions

of climate risk but rather with market demands [10] We found

that some communities in Mexico Nicaragua and Guatemala had

high migration rates as an additional characteristic of high

vulnerability While migration is definitely on the rise in many

parts of Mesoamerica it appeared that migration in coffee

households was difficult to link specifically to climate change

drivers [26]

In areas that will remain suitable for coffee growing but with

some reductions in suitability better agronomic management

could lessen the impacts of climate change [6] while in those

where a low suitability for coffee growing has been predicted

farmers will have to identify crop alternatives Solving the problem

of variable yield is crucial to the survival of farmers who live in

marginalized environments where agro-climatic conditions have

always been a challenge Diversification is therefore an important

strategy for production risk management in small farming systems

In general traditional agro-ecosystems are less vulnerable to

catastrophic loss because the wide variety of crops and various

spatial and temporal arrangements show compensation in case of

loss [27]

The adaptation of smallholders also relates to the global supply

chain which is governed by traders and industries that determine

the market price and requirements considering that an upgrading

of the coffee chain could help reduce its economic vulnerability

[28] But ultimately it is the farmers who decide what to farm and

how Strategic decisions must be made with the uncertainty of

climate conditions pricing costs government programs and

others factors [29]

Adaptation solutions should focus on the development of new

infrastructure policies and support institutions that facilitate

coordinate and maximize the benefits of the new systems of land

management and use This can be accomplished by improving

governance ensuring that development programs take climate

change into account increasing investments in irrigation infra-

structure and technologies that would increase water use

efficiency creating appropriate transportation and storage infra-

structure reviewing the agrarian property regime and establishing

accessible efficient markets for products assets and financial

services including insurance [30]Finance is critical access to

microloans and formal credit for farm-level investments will help

households strategically invest in coffee varieties complementary

crops and livelihood enhancements that effectively reduce risk and

improve social welfare [26]

Nevertheless the solutions require the support of social

organizations (civil society groups cooperatives and small-business

organizations) to enable rural households to access the resources

and knowledge necessary for adaptation while empowering

communities to shape the direction of the coffee sector to meet

their diverse development needs [26] In the case of coping with

weather-related hazards social networks play a primary role in

adaptation and recovery Social and institutional diversity itself

promotes resilience [25] The decision of a farmer to participate in

such an organization is thought to provide improved access to

resources and knowledge as well as to provide a social network

that could facilitate recovery and re-establishment of activities

following severe shocks (eg climatic and price changes)

Facilitating adaptation will involve renewed attention to helping

households acquire information about markets and new technol-

ogy In the face of declining public investment in agriculture it is

clear that public support is needed for research on low-cost low-

input strategies to manage climatic extremes [26] Nevertheless

investment is also needed in the social organization to enable the

adaptation of these strategies to local community and individual

family vulnerability characteristics

Table 6 Vulnerability indicators in relation to adaptation strategies and their specific adaption options

Vulnerability indicators Adaptation strategies Specific adaptations options

Decrease of suitability for coffee production Programs of research validation transferand adoption of agricultural technologiesthat adapt coffee to changing climate

Drip irrigation water harvesting and management of availablewater

High variability of annual productivity Management of shade fertility crop residues pest and diseases

Low soil fertility and forest conservation Conservation of soil water and natural forest

Low income diversification Improved varieties and hybrids

Diversification with other crops where loss of suitability for coffeeproduction

Poor health and nutrition Integral programs with Institutional supportimproving human and social resources

Improved environmental education (schools organizationscommittees)

Low level of organizational capacity Implementing food and health security programs

Low level of knowledge of polices ofcoffee sector and local laws

Provide cooperatives with social experts to improve the level ofparticipation of producers

High migration rate Empowering families in policies and laws of their environmentsector and to improve implementation

Low access to credit Implementation of long term financial ruralprograms

Financial education

Low viability of post harvest infrastructure Planning for the investment of resources

Low access to technologies Planning of long-term credits (in cash tools supplies and others)with technical assistance

Low access to transport and types of roads Implementation of investment programs toimprove road infrastructure quality housingand basic services

Planning with municipalities private sector internationalcooperation




The role of the state remains important for planned adaptation

and sustainable development Governance is vital in managing

global environmental risks and in promoting sustainable technol-

ogies Also it is necessary for the implementation of human and

social programs to have government support and participation of

organizations social networks education centres and the interna-

tional cooperation [31]

Supporting Information

File S1 Table S1 Key sustainable livelihood indicators

of sensitivity and adaptive capacity of coffee producing

families in Mesoamerica Table S2 Current and future

values of bioclimatic variables with (averages of coffee

growing area in each country) their coefficients of

variation (CV) of 19 GCM models for Nicaragua El

Salvador Guatemala and Mexico The CV indicates the

variation between models (ie the greater the CV the more

variability between GCMs)

(DOCX)

Acknowledgments

The authors give special thanks to the coffee growing families and the

organizations of each country Advice and guidance by Dr Tamara

Benjamin Carolina Backer and Wilfredo Chavez are gratefully acknowl-

edged We also acknowledge the support of colleagues at CIAT such as

Stephania Carmona Lorena Gomez Samuel Ocon Samuel Garcıa and

Dr Carlos Zealaya

Author Contributions

Conceived and designed the experiments MB PL JH Performed the

experiments MB PL JH OO Analyzed the data MB PL JH GS

Contributed reagentsmaterialsanalysis tools MB PL JH OO Wrote the

paper MB GS PL JH

References

1 DFID (Departament for Internacional Development UK) (1999) HojasOrientativas sobre los Medios de Vida Sostenibles Available httpcommunityeldisorg59c21877SP-GS1pdf and httpcommunityeldisorg59c21877SP-GS2pdf Accessed 15 October 2009

2 IPCC (Intergovermental Panel on Climate Change) (2007) Climate Change2007 Synthesis report Available httpwwwipccchpdfassessment-reportar4syrar4_syrpdf Accessed 2009 Oct 15

3 IPCC (Intergovermental Panel on Climate Change) (2001) Climate Change2001 Working Group II Impacts Adaptation and Vulnerability Summary forPolicy makersOMM-PNUMA Ginebra Suiza 95p

4 Gay C Estrada F Conde C Eakin H Villers L (2006) Potential impacts ofclimate change on agriculture A case of study of coffee production in VeracruzMexico Climatic Change 79259ndash288DOI101007s10584-006-9066-x

5 Baker P Haggar J (2007) Global Warming the impact on global coffeepresentation handout SCAA

6 Laderach P Lundy M Jarvis A Ramırez J Perez PE et al (2010a) Predictedimpact of climate change on coffee-supply chains In Leal Filho W (ed) TheEconomic social and Political Elements of Climate Change Springer VerlagBerlin DE 19 p

7 Davis AP Gole TW Baena S Moat J (2012) The Impact of Climate Change onIndigenous Arabica Coffee (Coffea arabica) Predicting Future Trends andIdentifying Priorities PLoS ONE 7(11) e47981 doi101371journalpone0047981

8 Jaramillo J Muchugu E Vega FE Davis A Borgemeister C et al (2011) SomeLike It Hot The Influence and Implications of Climate Change on Coffee BerryBorer (Hypothenemus hampei) and Coffee Production in East Africa PLoS ONE6(9) e24528

9 Tucker C Eakin H Castellanos E (2010) Perceptions of risk and adaptationCoffee producers market shocks and extreme weather in Central America andMexico Glob Environ Change 2023ndash32

10 CEPAL (Comision Economica para America Latina y el Caribe) (2002)Centroamerica El Impacto de la Caıda de los Precios del Cafe en el 2001Available http wwwfondominkachorlaviorgcafedocscepal2002pdf Ac-cessed 2009 Oct 15

11 Moguel P Toledo V (1999) Biodiversity conservation in traditional coffeesystems of Mexico Conservation Biology 13 (1)11ndash21

12 Escamilla E Ruiz O Dıaz G Landeros C Platas D et al (2005) Elagroecosistema cafe organico en Mexico Manejo Integrado de Plagas yAgroecologıa (Costa Rica) Nro76p5ndash16 2005

13 Haggar J Medina B Aguilar R Munoz C (2013) Land use change on coffeefarms in southern Guatemala and its Environmental Consequences Environ-mental Management DOI101007s00267-013-0019-7

14 Yohe G Malone E Brenkert A Schlesinger M Meij H et al (2006) lsquolsquoASynthetic Assessment of the Global Distribution of Vulnerability to ClimateChange from the IPCC Perspective that Reflects Exposure and AdaptiveCapacityrsquorsquo Palisades New York CIESIN (Center for International EarthScience Information Network) Columbia University Available httpsedacciesincolumbiaedumvaccv

15 Hijmans RJ Cameron SE Parra JL Jones PG Jarvis A (2005) Very highresolution interpolated climate surfaces for global land areas Int JClimatol2519651978 DOI101002joc1276

16 Ramırez J Jarvis A (2010) Disaggregation of Global Circulation Model OutputsAvailable httpwwwccafs-climateorgdowlandsdocsDisaggregation-WP-02pdf

17 Phillips SJ Anderson RP Schapire RE (2006) Maximum entropy modeling ofspecies geographic distributions Ecol Model 190231ndash259

18 Schroth G Ruf F (2013) Farmer strategies for tree crop diversification in thehumid tropics A review Agronomy for Sustainable Development in press

19 Geilfus F (1997) 80 Herramientas para el Desarrollo Participativo Diagnosticoplanificacion monitoreo y evaluacion IICA SAGAR Press Mexico 210p

20 Suarez Fabio (1999) Fundamentos de Estadıstica aplicado al sector agrope-cuario En Rojas Eberhard editores Santa Fe de Bogota 426p

21 Di Rienzo JA Casanoves F Balzarini MG Gonzalez L Tablada M et al (2008)InfoStat version 2008 FCA Universidad Nacional de Cordoba PressArgentina

22 Castro F Montes E Raine M (2004) Centroamerica la crisis cafetalera Efectos yestrategias para hacerle frente The World Bank Latin America and CaribbeanRegion Sustainable Development Working Paper 23

23 Laderach P Ovalle O Lau C Haggar J Eitzinger A et al (2012b) ClimateChange at Mesoamerican Origins In Oberthur T Laderach P Pohlan J andCock J editors Specialty Coffee Managing Quality International PlantNutrition Institute (IPNI) pp 39ndash49

24 Cutter S (1996) Vulnerability to environmental hazards Prog Hum Geogr 204(1996) pp 529ndash539

25 Adger WN Kelly PM (1999) Social Vulnerability to Climate Change and theArchitecture to Entitlements Mitigation and Adaptation Strategies for GlobalChangeVol 47Nro 4 253ndash266

26 Eakin H Bojorquez-Tapia LA Monterde DR Castellanos E Haggar J (2011)Adaptive Capacity and Social-Environmental ChangeTheoretical and Opera-tional Modeling of Smallholder Coffee Systems Response in MesoamericanPacific Rim Environmental Management (2011) 47352ndash367 DOI 101007s00267-010-9603-2

27 Altieri M Nicholls C (2009) Cambio Climatico y Agricultura CampesinaImpactos y respuestas adaptativas LEISA Vol 24 Universidad de CaliforniaBerkeley USA 4p

28 Dıaz R Heakin H Castellanos E Jimenez G (2009) Condiciones para laadaptacion de pequenos productores de cafe ante presiones economicasmediante procesos de lsquolsquoupgradingrsquorsquo en la cadena productiva RevistaIberoamericana de la Red de Economıa Ecologica Vol 1061ndash72

29 Smith B Mcnabb D Smithers J (1996) Agricultural adaptation to climaticvariation Clim Change 33 7ndash29 1996 23p

30 IPCC (Grupo Intergubernamental de Expertos sobre el Cambio Climatico)(2008) El Cambio Climatico y el Agua Documento Tecnico VIOMM-PNUMA Ginebra Suiza 224 p

31 Adger WN (2003) Social Capital Collective Action and Adaptation to ClimateChange Econ Geogr 79(4) 387ndash404 2003



An Integrated Framework for Assessing Vulnerability toClimate Change and Developing Adaptation Strategiesfor Coffee Growing Families in Mesoamerica

Marıa Baca1 Peter Laderach1 Jeremy Haggar2 Gotz Schroth3 Oriana Ovalle4

1 International Center for Tropical Agriculture (CIAT) Managua Nicaragua 2Natural Resources Institute (NRI) University of Greenwich Chatham Maritime Kent United

Kingdom 3 Rainforest Alliance Wageningen The Netherlands 4 International Center for Tropical Agriculture (CIAT) Cali Colombia

Abstract

The Mesoamerican region is considered to be one of the areas in the world most vulnerable to climate change Wedeveloped a framework for quantifying the vulnerability of the livelihoods of coffee growers in Mesoamerica at regional andlocal levels and identify adaptation strategies Following the Intergovernmental Panel on Climate Change (IPCC) conceptsvulnerability was defined as the combination of exposure sensitivity and adaptive capacity To quantify exposure changesin the climatic suitability for coffee and other crops were predicted through niche modelling based on historical climatedata and locations of coffee growing areas from Mexico Guatemala El Salvador and Nicaragua Future climate projectionswere generated from 19 Global Circulation Models Focus groups were used to identify nine indicators of sensitivity andeleven indicators of adaptive capacity which were evaluated through semi-structured interviews with 558 coffee producersExposure sensitivity and adaptive capacity were then condensed into an index of vulnerability and adaptation strategieswere identified in participatory workshops Models predict that all target countries will experience a decrease in climaticsuitability for growing Arabica coffee with highest suitability loss for El Salvador and lowest loss for Mexico Highvulnerability resulted from loss in climatic suitability for coffee production and high sensitivity through variability of yieldsand out-migration of the work force This was combined with low adaptation capacity as evidenced by poor post harvestinfrastructure and in some cases poor access to credit and low levels of social organization Nevertheless the specificcontributors to vulnerability varied strongly among countries municipalities and families making general trends difficult toidentify Flexible strategies for adaption are therefore needed Families need the support of government and institutionsspecialized in impacts of climate change and strengthening of farmer organizations to enable the adjustment of adaptationstrategies to local needs and conditions

Citation Baca M Laderach P Haggar J Schroth G Ovalle O (2014) An Integrated Framework for Assessing Vulnerability to Climate Change and DevelopingAdaptation Strategies for Coffee Growing Families in Mesoamerica PLoS ONE 9(2) e88463 doi101371journalpone0088463

Editor Ben Bond-Lamberty DOE Pacific Northwest National Laboratory United States of America

Received July 11 2013 Accepted January 7 2014 Published February 26 2014

Copyright 2014 Baca et al This is an open-access article distributed under the terms of the Creative Commons Attribution License which permits unrestricteduse distribution and reproduction in any medium provided the original author and source are credited

Funding This research was conducted under the CGIAR Research Program on Climate Change Agriculture and Food Security (CCAFS) in collaboration withCatholic Relief Services (CRS) and additional funding from Green Mountain Coffee Roasters (GMCR) Funds were used for employment and development ofresearch The funders had no role in study design data collection and analysis decision to publish or preparation of the manuscript There are no further patentsproducts in development or marketed products to declare

Competing Interests Funding from Green Mountain Coffee Roasters (GMCR) Funds were used for employment and development of research There are nofurther patents products in development or marketed products to declare This does not alter the authors9 adherence to all the PLOS ONE policies on sharingdata and materials as detailed online in the guide for authors

E-mail magubagogmailcom

Introduction

Climate change represents a serious threat for Mesoamerican

countries due to the multiple impacts predicted to directly affect

the population as well as various sectors of the economy [1] [2]

The Vulnerability-Resilience Indicators developed by Yohe

suggested high exposure to climate change for the Mesoamerican

and Caribbean Region [14] Climate projections indicate that

increases in temperature will reduce crop yields in general [3] and

particularly those of Arabica coffee one of the regionrsquos major

exports Arabica coffee responds strongly to seasonal temperature

patterns and coffee of high quality requires relatively stable

temperatures within a fairly narrow range [4] [5] Laderach and

others [6] predicted that optimal conditions for growing Arabica

coffee in Mesoamerica will move from currently 800 to 1400

masl upwards to 1200 to 1600 masl by 2050 Studies in

Ethiopia and Kenya have similarly foreseen significant impacts of

climate change on the distribution of wild coffee and coffee pests

[7] [8] in the later case extending the areas affected by coffee

berry-borer

During the last 40 years agriculture has contributed 10 of the

GDP in Latin American countries and is a major export earner It

is an important sector in the regional economy since it employs

30 to 40 of the economically active population and is essential

for the food security of the poorest segment of society [3] [9]

Across Mexico and Central America over 4 million people

depend directly on coffee production for their livelihoods [10]

According to CEPAL [9] coffee production purchasing and

processing employ an estimated 85 million people in the region

Employment and income generation from coffee are particularly

significant for many indigenous peoples in Mexico and Guate-

mala The environmental services generated by shade coffee

farms including carbon sequestration watershed services and the

conservation of biodiversity have also been highlighted by many





























change [1] [3]












adaptive capacity





































[16]



















(CV) among models















[1]

















Country

Department or

State

Exposure

level Total

High Medium Low



Madriz 4 14 14 32


Total 50 50 50 150


Santa Ana 8 0 0 8



Total 43 43 43 129


Solola 17 2 4 23



Total 43 43 43 129


Oaxaca 45 30 11 86


Total 50 50 50 150















2050 (Table 1)














Country

Changes in overall


production



above sea level


(1950ndash2005) (2050)

El Salvador 455 437 109 0 700 to 1700 1000 to 1700

Guatemala 129 255 542 74 600 to 1800 1200 to 2200

Mexico 182 346 469 03 500 to 2000 1200 to 2300

Nicaragua 353 321 325 01 700 to 1500 1000 to 1600








capacity [21]































Results



















hurricanes






























Mexico 23 46 31 100





































Mexico 38 38 24 100







the interviews




























































































































Country

Department or


High Medium Low


Matagalpa 9 22 6 38

Madriz 3 10 9 22


Total () 18 51 31 100


Santa Ana 2 5 0 6



Total () 14 68 18 100


Solola 9 9 0 18



Total () 22 64 15 100


Oaxaca 9 40 9 57


Total () 9 73 18 100
























farms (Table 6)






future conditions

Discussion


































drivers [26]













loss [27]








others factors [29]




















































Financial education

























(DOCX)

Acknowledgments






Dr Carlos Zealaya





paper MB GS PL JH

References





























































change [1] [3]












adaptive capacity





































[16]



















(CV) among models















[1]

















Country

Department or

State

Exposure

level Total

High Medium Low



Madriz 4 14 14 32


Total 50 50 50 150


Santa Ana 8 0 0 8



Total 43 43 43 129


Solola 17 2 4 23



Total 43 43 43 129


Oaxaca 45 30 11 86


Total 50 50 50 150















2050 (Table 1)














Country

Changes in overall


production



above sea level


(1950ndash2005) (2050)

El Salvador 455 437 109 0 700 to 1700 1000 to 1700

Guatemala 129 255 542 74 600 to 1800 1200 to 2200

Mexico 182 346 469 03 500 to 2000 1200 to 2300

Nicaragua 353 321 325 01 700 to 1500 1000 to 1600








capacity [21]































Results



















hurricanes






























Mexico 23 46 31 100





































Mexico 38 38 24 100







the interviews




























































































































Country

Department or


High Medium Low


Matagalpa 9 22 6 38

Madriz 3 10 9 22


Total () 18 51 31 100


Santa Ana 2 5 0 6



Total () 14 68 18 100


Solola 9 9 0 18



Total () 22 64 15 100


Oaxaca 9 40 9 57


Total () 9 73 18 100
























farms (Table 6)






future conditions

Discussion


































drivers [26]













loss [27]








others factors [29]




















































Financial education

























(DOCX)

Acknowledgments






Dr Carlos Zealaya





paper MB GS PL JH

References




































adaptive capacity





































[16]



















(CV) among models















[1]

















Country

Department or

State

Exposure

level Total

High Medium Low



Madriz 4 14 14 32


Total 50 50 50 150


Santa Ana 8 0 0 8



Total 43 43 43 129


Solola 17 2 4 23



Total 43 43 43 129


Oaxaca 45 30 11 86


Total 50 50 50 150















2050 (Table 1)














Country

Changes in overall


production



above sea level


(1950ndash2005) (2050)

El Salvador 455 437 109 0 700 to 1700 1000 to 1700

Guatemala 129 255 542 74 600 to 1800 1200 to 2200

Mexico 182 346 469 03 500 to 2000 1200 to 2300

Nicaragua 353 321 325 01 700 to 1500 1000 to 1600








capacity [21]































Results



















hurricanes






























Mexico 23 46 31 100





































Mexico 38 38 24 100







the interviews




























































































































Country

Department or


High Medium Low


Matagalpa 9 22 6 38

Madriz 3 10 9 22


Total () 18 51 31 100


Santa Ana 2 5 0 6



Total () 14 68 18 100


Solola 9 9 0 18



Total () 22 64 15 100


Oaxaca 9 40 9 57


Total () 9 73 18 100
























farms (Table 6)






future conditions

Discussion


































drivers [26]













loss [27]








others factors [29]




















































Financial education

























(DOCX)

Acknowledgments






Dr Carlos Zealaya





paper MB GS PL JH

References













































2050 (Table 1)














Country

Changes in overall


production



above sea level


(1950ndash2005) (2050)

El Salvador 455 437 109 0 700 to 1700 1000 to 1700

Guatemala 129 255 542 74 600 to 1800 1200 to 2200

Mexico 182 346 469 03 500 to 2000 1200 to 2300

Nicaragua 353 321 325 01 700 to 1500 1000 to 1600








capacity [21]































Results



















hurricanes






























Mexico 23 46 31 100





































Mexico 38 38 24 100







the interviews




























































































































Country

Department or


High Medium Low


Matagalpa 9 22 6 38

Madriz 3 10 9 22


Total () 18 51 31 100


Santa Ana 2 5 0 6



Total () 14 68 18 100


Solola 9 9 0 18



Total () 22 64 15 100


Oaxaca 9 40 9 57


Total () 9 73 18 100
























farms (Table 6)






future conditions

Discussion


































drivers [26]













loss [27]








others factors [29]




















































Financial education

























(DOCX)

Acknowledgments






Dr Carlos Zealaya





paper MB GS PL JH

References





































capacity [21]































Results



















hurricanes






























Mexico 23 46 31 100





































Mexico 38 38 24 100







the interviews




























































































































Country

Department or


High Medium Low


Matagalpa 9 22 6 38

Madriz 3 10 9 22


Total () 18 51 31 100


Santa Ana 2 5 0 6



Total () 14 68 18 100


Solola 9 9 0 18



Total () 22 64 15 100


Oaxaca 9 40 9 57


Total () 9 73 18 100
























farms (Table 6)






future conditions

Discussion


































drivers [26]













loss [27]








others factors [29]




















































Financial education

























(DOCX)

Acknowledgments






Dr Carlos Zealaya





paper MB GS PL JH

References


































































Mexico 38 38 24 100







the interviews




























































































































Country

Department or


High Medium Low


Matagalpa 9 22 6 38

Madriz 3 10 9 22


Total () 18 51 31 100


Santa Ana 2 5 0 6



Total () 14 68 18 100


Solola 9 9 0 18



Total () 22 64 15 100


Oaxaca 9 40 9 57


Total () 9 73 18 100
























farms (Table 6)






future conditions

Discussion


































drivers [26]













loss [27]








others factors [29]




















































Financial education

























(DOCX)

Acknowledgments






Dr Carlos Zealaya





paper MB GS PL JH

References





































the interviews




























































































































Country

Department or


High Medium Low


Matagalpa 9 22 6 38

Madriz 3 10 9 22


Total () 18 51 31 100


Santa Ana 2 5 0 6



Total () 14 68 18 100


Solola 9 9 0 18



Total () 22 64 15 100


Oaxaca 9 40 9 57


Total () 9 73 18 100
























farms (Table 6)






future conditions

Discussion


































drivers [26]













loss [27]








others factors [29]




















































Financial education

























(DOCX)

Acknowledgments






Dr Carlos Zealaya





paper MB GS PL JH

References
































































































































Country

Department or


High Medium Low


Matagalpa 9 22 6 38

Madriz 3 10 9 22


Total () 18 51 31 100


Santa Ana 2 5 0 6



Total () 14 68 18 100


Solola 9 9 0 18



Total () 22 64 15 100


Oaxaca 9 40 9 57


Total () 9 73 18 100
























farms (Table 6)






future conditions

Discussion


































drivers [26]













loss [27]








others factors [29]




















































Financial education

























(DOCX)

Acknowledgments






Dr Carlos Zealaya





paper MB GS PL JH

References






















































farms (Table 6)






future conditions

Discussion


































drivers [26]













loss [27]








others factors [29]




















































Financial education

























(DOCX)

Acknowledgments






Dr Carlos Zealaya





paper MB GS PL JH

References










































drivers [26]













loss [27]








others factors [29]




















































Financial education

























(DOCX)

Acknowledgments






Dr Carlos Zealaya





paper MB GS PL JH

References



















































(DOCX)

Acknowledgments






Dr Carlos Zealaya





paper MB GS PL JH

References


































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